Autonomous Tillage Shank Malfunction Detection via Machine Learning
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Solution Overview
Problem
Existing autonomous farming systems lack effective real-time monitoring and detection capabilities for malfunctions in tilling assemblies, leading to reduced tilling quality and potential equipment damage due to detached components or plugs, especially under varying environmental conditions.
Innovation Solution
A detection system utilizing machine-learned models and sensors, such as cameras and thermal sensors, to continuously monitor the tilling assembly for malfunctions, including loose or missing shanks and plugged sweeps, providing real-time data analysis and recommendations for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of tilling assembly is used, then system complexity is reduced, but detection precision and response rate of malfunctions decrease
Solution Approach 1:
The patent replaces manual visual inspection with automated image capture systems (cameras) and machine learning models to detect shank malfunctions. The system automatically captures images of the tilling assembly, processes them through trained models, and identifies malfunctioning shanks without human intervention, thereby improving detection precision while accepting increased system complexity.
2Productivity
If monitoring rate is increased to detect malfunctions faster, then productivity is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic image capture at predetermined time intervals during autonomous operation. This periodic monitoring approach enables continuous detection capability and high productivity while managing energy consumption by not operating continuously, but rather at strategically spaced intervals that maintain effective monitoring coverage.
3Reliability
If real-time monitoring is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by capturing images and processing them through pre-trained machine learning models during autonomous operation. The models are trained in advance on labeled images of malfunctioning and operational shanks, enabling reliable real-time detection without requiring complex runtime decision-making, thus improving reliability while managing complexity through pre-computed knowledge.
4Measurement precision
If machine learning models are used for detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detection system is segmented into distinct functional modules: image capture by cameras, preprocessing of images, application of specialized machine learning models for shank detection, and interpretation of results. This segmentation allows each component to be optimized independently and facilitates the use of complex AI algorithms without overwhelming the overall system architecture, thereby improving precision while managing complexity through modular design.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the detection rate and accuracy of malfunctions, allowing for timely interventions and improving the overall quality of autonomous farming routines by identifying and addressing issues promptly, even under challenging environmental conditions.
Implementation Method 1
a monochrome camera captures light reflected off of reflective markers, each marker coupled to a tilling shank
Implementation Method 2
a thermal camera captures thermal radiation from below-ground tilling sweeps
Data Source
AI summary
A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).